In the actual algorithm, we used the MAGSAC++ quality function [1]} and, thus, \(\epsilon \)  is not an inlier-outlier threshold but its loose upper bound.
We also tested Tukey-bisquare, Huber, redescending Huber, and MSAC losses. While Tukey-bisquare and MSAC lead to reasonably accurate solutions, MAGSAC++ leads to the most accurate results on all tested problems.
The setting of \(q_{\text{min}}\)  is easy, \(q_{\text{min}} = 20\)  works for most of the problems which we tested.
Also, instead of inserting a single instance in \(\mathcal {I}\) , we add multiple ones in each iteration.
In all our tests, adding 10 instances worked well.
